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  "path": "/t/announcing-jneopallium-biologically-grounded-neuron-networks-with-industrial-grade-safety-for-real-plants/175842#post_1",
  "publishedAt": "2026-05-08T01:48:39.000Z",
  "site": "https://discuss.huggingface.co",
  "tags": [
    "https://github.com/rakovpublic/jneopallium",
    "https://gitlab.com/rakovpublic/jneopallium"
  ],
  "textContent": "Hi Hugging Face community!Today I’m excited to share Jneopallium — a Java framework for building biologically-grounded, multi-timescale neuron networks that can actually run as safety-gated controllers in chemical plants, power grids, manufacturing lines, water treatment, and HVAC systems.While most of the ecosystem focuses on Python/PyTorch models for the cloud or edge inference, Jneopallium was designed from the ground up for industrial operational technology (OT) — where a single bad write to an actuator can have serious consequences.What makes it different\n\n  * True biological grounding: Typed signals with independent propagation, receptor heterogeneity, dual fast/slow processing loops (regulatory vs supervisory timescales), and per-signal frequency control via ProcessingFrequency(loop, epoch). It’s not just “bio-inspired” — the architecture mirrors how natural neuron networks actually work.\n\n  * Industrial OPC UA bridge (Eclipse Milo-based): Full read/write integration with any standards-compliant PLC, SCADA, or digital twin. Subscriptions, alarms & conditions, quality propagation, wall-clock timestamps from the server — all handled cleanly.\n\n  * Six non-negotiable safety invariants baked into the bridge:\n\n    1. No raw actuator writes — everything goes through Planning → SafetyGate → Interlock → OperatorOverride → Aggregator.\n\n    2. Interlocks have absolute authority (fail-safe write on trip).\n\n    3. Operator override always wins for regulatory control.\n\n    4. Every single write is audited (JSONL + optional OPC UA mirror).\n\n    5. Quality never silently promoted.\n\n    6. Timestamps come from the plant, not the JVM.\n\n  * Progressive autonomy done right: Per-loop SHADOW → ADVISORY → AUTONOMOUS commissioning sequence. You can run 90 % of loops fully autonomous while keeping 1 % in full shadow mode. No dangerous global “AI on/off” switch.\n\n  * Built-in safety neurons: SafetyGateNeuron, InterlockNeuron, Human-harm discriminator (five-dimensional consequence model), OscillationMonitor, etc.\n\n\n\n\nLicense: BSD 3-Clause (fully open).Quick start (industrial smoke test)\n\nyaml\n\n\n    # Minimal unsecured demo config (public Milo server)\n    connection:\n      endpointUrl: \"opc.tcp://milo.digitalpetri.com:62541/milo\"\n    ...\n\n\nAdd the single Maven dependency, drop in the YAML, run the bootstrap class, and you’re connected. Full manual (with worked temperature-loop example, cascaded loops, audit format, etc.) is included in the repo.Links\n\n  * GitHub: https://github.com/rakovpublic/jneopallium\n\n  * GitLab mirror + Maven packages: https://gitlab.com/rakovpublic/jneopallium\n\n  * Full OPC UA Integration Manual: see JNEOPALLIUM_OPCUA_INTEGRATION.md (or the attached .docx version in releases)\n\n\n\n\nJneopallium is still early (1.0-SNAPSHOT) but already battle-tested in concept for real industrial use. The core framework is mature; the industrial module is production-ready for controlled commissioning.I’d love feedback from the HF community — especially from people working on:\n\n  * Safe autonomous agents\n\n  * Industrial / robotics control\n\n  * Multi-timescale or biologically-plausible architectures\n\n  * OT cybersecurity & functional safety\n\n\n\n\nIf you’re in process control, functional safety, or just curious about neuron nets that can actually touch physical hardware without blowing up the plant — come say hi!Star the repo if this resonates, try the demo, and let me know what you think.— Dmytro Rakovskyi",
  "title": "Announcing Jneopallium: Biologically-Grounded Neuron Networks with Industrial-Grade Safety for Real Plants"
}